Gastos Casa
10 abril, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 26/2/2023 | Electricidad | 40440 | Andrés | Enel |
| 26/2/2023 | Comida | 18480 | Tami | Café Turri Valpo |
| 26/2/2023 | Uber | 9602 | Tami | NA |
| 27/2/2023 | Comida | 9090 | Tami | Copec |
| 27/2/2023 | Bencina + tag | 40000 | Tami | NA |
| 27/2/2023 | Comida | 62535 | Tami | NA |
| 1/3/2023 | Uber | 2960 | Andrés | uber jueves reñaca-viña tarde |
| 1/3/2023 | Uber | 3433 | Andrés | uber martes tarde reñaca |
| 1/3/2023 | Uber | 3308 | Andrés | viaje lunes noche |
| 1/3/2023 | Comida | 7200 | Andrés | Frutos secos |
| 5/3/2023 | Comida | 62296 | Tami | NA |
| 6/3/2023 | Enceres | 110000 | Andrés | arreglo reja |
| 9/3/2023 | Forro cortina ducha | 2490 | Tami | NA |
| 4/3/2023 | Microondas regalo | 40000 | Tami | NA |
| 9/3/2023 | Comida | 106490 | Tami | Soul Bar |
| 9/3/2023 | Comida | 27642 | Tami | NA |
| 13/3/2023 | Comida | 51473 | Tami | NA |
| 13/3/2023 | Diosi | 20990 | Tami | Antiparasitario |
| 16/3/2023 | Vacunas Influenza | 19980 | Tami | NA |
| 20/3/2023 | Comida | 52314 | Tami | NA |
| 26/3/2023 | Comida | 24970 | Andrés | caramagnola |
| 28/3/2023 | Comida | 71805 | Tami | NA |
| 29/3/2023 | Electricidad | 42447 | Andrés | PAC ENEL 01686518 |
| 30/3/2023 | Netflix | 8320 | Tami | NA |
| 31/3/2023 | Comida | 13226 | Tami | NA |
| 31/3/2023 | Comida | 100000 | Andrés | wild foods |
| 31/3/2023 | Enceres | 15400 | Tami | Incoludido |
| 9/4/2023 | Gas | 67300 | Andrés | el de la derecha |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 6.0351e+08 2 6.2707 0.002 **
## lag_depvar 8.3400e+10 1 1733.1180 <2e-16 ***
## Residuals 2.7188e+10 565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 986.9765 13470.70 0.0183575
## 2-0 28038.396 22330.3084 33746.48 0.0000000
## 2-1 20809.558 17403.1888 24215.93 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 33 31622.57 0 27223.29
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## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 413 50272.66 15512.960
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1990.92054 4028.38841 -526.14156 2448.41408 -2946.04026 527.32090
## 8 9 10 11 12 13
## -5643.24077 -1201.93223 -3981.73055 -448.42470 -4965.81480 -1653.79109
## 14 15 16 17 18 19
## -943.83121 337.14381 -3273.67777 -418.08105 -2164.46586 6566.28668
## 20 21 22 23 24 25
## -1527.58304 -1211.82649 1469.15906 -1182.50757 234.97075 1698.98731
## 26 27 28 29 30 31
## -7087.71516 928.05187 8182.66633 452.44722 20.66612 -2367.67811
## 32 33 34 35 36 37
## 1595.90750 4600.11169 1176.08791 2442.63282 -1808.86654 4653.18575
## 38 39 40 41 42 43
## 4330.45065 -2230.80767 -2953.06730 -1099.67822 -10737.49671 7240.55817
## 44 45 46 47 48 49
## 2550.41048 1373.56484 8117.93100 737.12674 6576.89237 6788.58728
## 50 51 52 53 54 55
## -5784.45772 -4738.38791 -5033.04750 -7929.71833 6090.32263 -4080.97452
## 56 57 58 59 60 61
## -4917.83346 3811.57063 868.19418 -44.74744 131.22873 -5005.14644
## 62 63 64 65 66 67
## 18094.97687 3701.58996 -3574.84056 5970.11705 7412.38772 14734.81211
## 68 69 70 71 72 73
## 1848.89460 -13068.01281 -1243.76466 4692.00493 -4834.92168 -4370.96672
## 74 75 76 77 78 79
## -10489.21197 2423.42936 -5425.24955 1015.66217 -6902.74215 482.08534
## 80 81 82 83 84 85
## -2408.19879 -2751.78116 -3995.18047 -611.52904 2247.69066 3715.70758
## 86 87 88 89 90 91
## 454.30637 -501.79801 179.37406 4288.01870 -1154.13214 1153.18064
## 92 93 94 95 96 97
## -2056.64407 -1047.23790 170.17420 269.39076 -7487.19912 2353.07721
## 98 99 100 101 102 103
## -8624.42276 -3000.89944 -4105.89243 -1813.75686 -1336.51176 3109.65815
## 104 105 106 107 108 109
## -2388.65023 2542.31717 -1190.78082 936.86052 2562.68356 -3162.98395
## 110 111 112 113 114 115
## -4745.51770 -892.37573 1862.93375 11667.55219 -1208.63701 2692.45802
## 116 117 118 119 120 121
## 4296.91133 3553.33179 -1038.45200 -4667.58445 -3703.93130 2319.75692
## 122 123 124 125 126 127
## -1721.13406 1342.45435 8866.81430 897.67139 179.04581 -2477.85250
## 128 129 130 131 132 133
## 2681.14965 7088.44710 1078.19583 -8436.75206 1763.19960 4156.47269
## 134 135 136 137 138 139
## -3125.39424 -1401.03211 -844.20382 -3875.29752 1168.73450 -501.97873
## 140 141 142 143 144 145
## -2921.38834 1697.58958 -1890.48975 -7846.32780 1987.08316 -3515.38156
## 146 147 148 149 150 151
## 2054.51067 -288.82227 994.59683 -379.01046 1333.44120 1176.96997
## 152 153 154 155 156 157
## 3354.06226 -4847.58770 -1185.28050 -3250.68308 5928.35577 9750.81092
## 158 159 160 161 162 163
## -3301.24980 -4656.68968 3711.37645 329.91465 2839.03599 -5749.20058
## 164 165 166 167 168 169
## -6610.95834 4267.61812 17532.46598 3848.26337 -167.66486 -2223.81231
## 170 171 172 173 174 175
## -898.62442 3787.42933 -18.08207 -7870.73848 3028.76652 4504.39236
## 176 177 178 179 180 181
## 822.87244 8947.44337 -9016.19646 -3287.70898 -10577.30428 -11119.43682
## 182 183 184 185 186 187
## 1312.73577 9388.63585 -1284.42555 6070.98496 6725.95160 13354.75365
## 188 189 190 191 192 193
## 8675.89464 -3797.04272 2696.54232 10597.69727 -1384.72105 -2209.04528
## 194 195 196 197 198 199
## -10068.26787 -6202.52746 1366.37017 -5093.29295 -9675.73427 5468.84929
## 200 201 202 203 204 205
## -2951.25170 -1602.40050 -694.69991 6606.75848 10024.18537 757.71718
## 206 207 208 209 210 211
## 3100.59227 3280.28656 5973.05865 13038.39126 -5441.34702 -11086.08837
## 212 213 214 215 216 217
## -5508.69096 -10452.57180 -4978.83897 1611.72345 -12909.70400 16447.62071
## 218 219 220 221 222 223
## 7943.67086 1693.81852 26856.80404 12790.83686 7628.39984 14325.70997
## 224 225 226 227 228 229
## -3584.42002 -1453.07177 4038.35733 619.36806 2991.91603 9248.58185
## 230 231 232 233 234 235
## 6098.20281 -1628.74913 -1573.34284 9660.38764 -11248.08294 -7082.13212
## 236 237 238 239 240 241
## -8374.81766 -9970.23797 3172.62919 1467.06363 -8171.48842 -8891.78176
## 242 243 244 245 246 247
## 9165.96704 -7648.11064 2580.01354 -10190.69330 -3976.70416 1494.90986
## 248 249 250 251 252 253
## 1091.43056 -12215.31602 3704.39155 2149.14953 4314.30268 2258.57936
## 254 255 256 257 258 259
## -1026.28793 11270.17823 21052.87074 3436.03086 -4037.42273 4312.99720
## 260 261 262 263 264 265
## -1487.18001 3927.56997 -4656.67473 -10724.78021 -4603.41659 -411.96441
## 266 267 268 269 270 271
## -5076.98505 8874.08573 -4150.00493 4304.15911 -1976.75361 4552.76218
## 272 273 274 275 276 277
## 845.65943 7439.48586 -1254.42326 12171.74947 -4404.74438 1878.92713
## 278 279 280 281 282 283
## -219.85707 7996.38653 -4893.12962 -2590.02980 -11131.68261 -2574.24402
## 284 285 286 287 288 289
## 18746.72109 7935.57258 2903.28598 -460.15912 1063.94865 6551.78200
## 290 291 292 293 294 295
## 7048.00726 -18595.37508 -11022.31233 -8032.13753 9740.46776 3184.37279
## 296 297 298 299 300 301
## -1054.68895 27524.84551 10261.63582 5111.78385 9727.63665 3077.29185
## 302 303 304 305 306 307
## -819.75212 8092.43311 -24090.16673 -3400.96319 -48.31032 -6838.33937
## 308 309 310 311 312 313
## -3855.57831 3045.16584 -9064.95708 -3119.15913 -8073.54041 1668.62768
## 314 315 316 317 318 319
## -3033.17460 2167.18159 -3951.44006 27571.96821 -550.17193 3455.89153
## 320 321 322 323 324 325
## 10996.54880 5773.29494 32568.21747 5363.24511 -20690.93928 1975.79124
## 326 327 328 329 330 331
## 1291.20004 -6287.89243 -1576.16300 -33114.64523 1000.46445 -2164.59559
## 332 333 334 335 336 337
## 53.89880 -3008.42896 4248.99102 -256.46537 -6766.71084 -2938.50991
## 338 339 340 341 342 343
## -2012.05073 -7496.62392 4027.14056 -1180.94861 -1545.01792 -799.53973
## 344 345 346 347 348 349
## 373.67669 682.82826 -1413.89654 -9243.43004 -13022.49862 2478.51110
## 350 351 352 353 354 355
## -4141.99953 -3478.26254 -5799.03517 1928.17525 1571.52115 2946.61781
## 356 357 358 359 360 361
## -3566.75251 -322.52344 871.79011 7210.24713 484.01641 167.16936
## 362 363 364 365 366 367
## 2786.27395 -2544.85186 -677.30856 -8544.19149 -4436.89631 -6024.77563
## 368 369 370 371 372 373
## -4764.61420 -7067.78534 5196.64376 564.88744 7316.90397 -7428.35719
## 374 375 376 377 378 379
## -2064.39963 -3188.88950 -2268.70565 -12258.21247 2090.04236 -10438.15052
## 380 381 382 383 384 385
## 5882.96463 9535.48701 3338.04606 -2185.31968 1812.27670 6951.57911
## 386 387 388 389 390 391
## 11622.53317 -5584.75118 -5164.12495 28.16661 8745.90338 2004.04726
## 392 393 394 395 396 397
## 11406.97694 -9687.81332 2943.29355 881.02451 727.75968 -490.94153
## 398 399 400 401 402 403
## -403.46240 -14329.78610 8670.99669 -1014.09784 -1203.16771 7153.20526
## 404 405 406 407 408 409
## -7751.80575 -1121.79092 -2352.11141 -5636.09410 -2674.46760 -3727.70753
## 410 411 412 413 414 415
## -8562.53780 6325.06732 1843.34426 -7166.73084 -7489.69335 14421.15808
## 416 417 418 419 420 421
## 4028.49059 4701.89043 -7828.03800 -4545.45762 -2403.64501 3019.19110
## 422 423 424 425 426 427
## -13806.15424 -2595.62110 -8898.66660 3211.76973 7184.34338 6790.13125
## 428 429 430 431 432 433
## -3768.95948 -3906.97370 -4510.93302 -1580.13172 -5501.04544 -6417.91774
## 434 435 436 437 438 439
## -5742.84444 -1187.00309 -637.74418 -4761.77970 2793.92780 5057.10164
## 440 441 442 443 444 445
## -4833.97266 -1939.80936 1794.37797 -3616.39479 3053.67396 -6356.35678
## 446 447 448 449 450 451
## -11894.14402 -4303.95256 9853.81826 -1808.57976 4977.65323 -5639.84983
## 452 453 454 455 456 457
## -898.40011 608.90342 3253.39718 -12037.32536 3587.16789 -6476.32799
## 458 459 460 461 462 463
## 6741.61829 3241.64132 2739.90970 -3611.02322 2322.14335 224.24584
## 464 465 466 467 468 469
## 2023.76028 -289.33785 3580.44843 -2406.57765 6033.82396 -6707.10132
## 470 471 472 473 474 475
## -2737.06984 -1978.98742 -4436.91361 3221.18842 8028.58456 -5775.96040
## 476 477 478 479 480 481
## 1717.97409 -5942.12246 -2613.76442 2241.95266 -12694.83740 -9534.78407
## 482 483 484 485 486 487
## -989.79816 237.73255 -739.21128 -1115.72457 -9356.56557 11314.16309
## 488 489 490 491 492 493
## 6473.62268 7667.32630 -5181.74387 5612.15219 9543.22304 6312.51315
## 494 495 496 497 498 499
## -13212.79886 -10326.98151 -3214.81625 -880.05781 -295.86525 -7394.48214
## 500 501 502 503 504 505
## 836.41802 4519.10000 5749.43627 909.78599 330.37515 -6989.61259
## 506 507 508 509 510 511
## 809.71620 -4805.72407 2070.03918 -1053.80301 -7915.37571 -368.96927
## 512 513 514 515 516 517
## -2438.45689 -352.17944 1570.22886 -9252.64127 -7534.14683 24510.64677
## 518 519 520 521 522 523
## 10087.51126 6151.13100 -5062.38078 3057.52589 17277.72503 11749.62120
## 524 525 526 527 528 529
## -23867.49336 -4820.78412 -3501.81242 4799.64725 -121.68052 -10868.29830
## 530 531 532 533 534 535
## 4611.59558 14139.33221 -4727.68587 4612.48574 5797.67799 -1543.47194
## 536 537 538 539 540 541
## -4300.13152 -6841.05172 -1876.08906 8547.30497 365.81156 -7903.05198
## 542 543 544 545 546 547
## 2043.72616 -366.74707 599.96864 -10795.10503 -10839.10924 2253.03265
## 548 549 550 551 552 553
## 7225.55564 -1081.16489 1073.66748 -7480.75028 8796.05896 1152.64323
## 554 555 556 557 558 559
## -11697.21320 9392.60748 8902.52826 353.73637 5104.05455 -3320.46337
## 560 561 562 563 564 565
## 14353.37272 21758.91688 -6186.22410 -9421.05701 7014.72240 471.42166
## 566 567 568 569 570
## 3695.66937 -7135.20326 -17078.05375 6872.59901 6660.54311
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17278.37 20110.61 24342.28 24061.73 26402.75 23749.39 24461.96 19719.08
## 10 11 12 13 14 15 16 17
## 19457.02 16813.71 17587.10 14333.65 14384.55 15045.71 16733.39 15062.22
## 18 19 20 21 22 23 24 25
## 16091.47 15468.28 22513.58 21602.40 21084.98 22965.08 22294.60 22943.73
## 26 27 28 29 30 31 32 33
## 24780.00 18740.23 20457.33 28253.55 28310.91 27985.54 25627.38 27022.46
## 34 35 36 37 38 39 40 41
## 30845.34 31191.94 32593.72 30117.39 34112.55 37303.81 34375.35 31202.96
## 42 43 44 45 46 47 48 49
## 30056.78 20685.73 28165.02 30588.72 31672.21 38474.44 37971.68 42609.41
## 50 51 52 53 54 55 56 57
## 46823.46 39559.67 34156.62 29205.43 22385.82 28642.83 25241.40 21558.43
## 58 59 60 61 62 63 64 65
## 25943.66 27196.60 27492.06 27901.72 23794.31 40298.55 42132.84 37403.74
## 66 67 68 69 70 71 72 73
## 41588.61 46478.47 57090.68 55114.87 40435.48 37954.42 40956.49 35286.54
## 74 75 76 77 78 79 80 81
## 30762.64 21514.86 24699.54 20646.62 22721.74 17644.06 19648.91 18879.50
## 82 83 84 85 86 87 88 89
## 17912.32 15991.39 17262.45 20851.58 25246.12 26230.80 26255.63 26869.12
## 90 91 92 93 94 95 96 97
## 30972.56 29809.25 30803.36 28877.95 28081.97 28448.18 28852.63 22463.78
## 98 99 100 101 102 103 104 105
## 25462.99 18530.04 17392.18 15443.19 15741.37 16415.20 20864.36 19952.68
## 106 107 108 109 110 111 112 113
## 23445.35 23236.43 24903.75 27765.41 25276.66 21738.80 22012.78 24645.16
## 114 115 116 117 118 119 120 121
## 35452.64 33654.97 35482.80 38465.38 40411.02 38111.58 32959.79 29320.39
## 122 123 124 125 126 127 128 129
## 31392.28 29681.26 30856.61 38416.47 38060.81 37127.28 34007.28 35779.12
## 130 131 132 133 134 135 136 137
## 41148.66 40591.89 31839.80 33097.96 36270.97 32700.46 31096.20 30186.01
## 138 139 140 141 142 143 144 145
## 26761.12 28168.12 27938.96 25637.41 27651.20 26283.18 19918.92 22933.52
## 146 147 148 149 150 151 152 153
## 20771.63 23733.11 24270.26 25852.30 26033.42 27678.89 28972.79 31989.02
## 154 155 156 157 158 159 160 161
## 27482.99 26749.83 24317.93 30181.05 41321.68 39660.69 37039.48 42033.37
## 162 163 164 165 166 167 168 169
## 43434.54 46832.49 42322.24 37654.10 43050.82 59267.31 61467.81 59890.24
## 170 171 172 173 174 175 176 177
## 56732.62 55140.28 57828.65 56857.88 49190.52 51999.18 55722.13 55758.13
## 178 179 180 181 182 183 184 185
## 62849.48 53401.71 50169.73 41026.72 32610.55 36100.36 46150.71 45609.59
## 186 187 188 189 190 191 192 193
## 51531.05 57245.82 67972.11 73227.19 66955.03 67147.45 74180.58 69879.76
## 194 195 196 197 198 199 200 201
## 65426.13 54726.53 48788.06 50204.86 45822.73 38032.72 44423.68 42660.40
## 202 203 204 205 206 207 208 209
## 42300.27 42776.10 49534.39 58376.85 58008.41 59724.14 61371.23 65142.47
## 210 211 212 213 214 215 216 217
## 74559.20 66683.66 54934.83 49572.00 40615.70 37589.42 40686.70 30759.38
## 218 219 220 221 222 223 224 225
## 47643.61 54925.90 55823.05 78468.73 85924.31 87917.00 95468.42 86466.93
## 226 227 228 229 230 231 232 233
## 80496.93 80081.06 76748.66 75914.56 80626.65 81983.75 76448.49 71686.61
## 234 235 236 237 238 239 240 241
## 77310.51 64028.56 56106.96 48099.95 39755.66 43925.51 46066.92 39552.07
## 242 243 244 245 246 247 248 249
## 33264.89 43493.25 37770.42 41685.41 33989.99 32702.66 36338.71 39147.74
## 250 251 252 253 254 255 256 257
## 30025.47 35932.28 39713.70 44881.13 47585.15 47080.39 57327.13 74732.25
## 258 259 260 261 262 263 264 265
## 74548.28 67894.15 69368.18 65608.86 67047.39 60837.92 50168.99 46217.25
## 266 267 268 269 270 271 272 273
## 46425.56 42552.77 51310.58 47603.27 51728.18 49854.67 53900.63 54195.09
## 274 275 276 277 278 279 280 281
## 60180.85 57827.54 67449.60 61406.36 61615.29 59973.04 65685.70 59449.17
## 282 283 284 285 286 287 288 289
## 56031.11 45638.39 44043.56 61185.14 66686.14 67093.44 64524.62 63616.79
## 290 291 292 293 294 295 296 297
## 67596.71 71486.38 52582.88 42736.99 36779.53 47046.63 50271.40 49390.01
## 298 299 300 301 302 303 304 305
## 73459.08 79373.22 80037.36 84625.57 82833.61 77890.00 81338.60 56369.39
## 306 307 308 309 310 311 312 313
## 52650.17 52331.63 46154.44 43378.55 46962.96 39554.30 38283.11 32873.23
## 314 315 316 317 318 319 320 321
## 36637.89 35823.53 39634.87 37629.89 63280.74 61133.25 62748.31 70704.42
## 322 323 324 325 326 327 328 329
## 73079.21 98427.04 96813.22 72770.35 71574.51 69940.46 61934.45 59071.79
## 330 331 332 333 334 335 336 337
## 29177.96 32846.17 33283.39 35591.14 34935.44 40672.18 41742.14 37014.65
## 338 339 340 341 342 343 344 345
## 36233.19 36359.20 31702.72 37670.23 38330.16 38587.25 39458.47 41235.03
## 346 347 348 349 350 351 352 353
## 43047.47 42800.43 35782.07 26399.35 31716.00 30582.98 30175.18 27804.11
## 354 355 356 357 358 359 360 361
## 32458.48 36193.10 40633.32 38831.81 40085.50 42212.75 49569.27 50116.97
## 362 363 364 365 366 367 368 369
## 50317.58 52767.85 50264.45 49711.91 42395.61 39607.06 35804.04 33594.36
## 370 371 372 373 374 375 376 377
## 29672.78 36922.54 39197.52 47041.79 41044.97 40495.03 39039.99 38575.21
## 378 379 380 381 382 383 384 385
## 29490.67 34064.72 27152.75 35329.08 45608.10 49154.89 47437.29 49418.56
## 386 387 388 389 390 391 392 393
## 55606.18 65042.04 58288.84 52785.98 52516.10 59857.10 60377.74 69001.10
## 394 395 396 397 398 399 400 401
## 58163.71 59722.40 59284.81 58771.37 57266.18 56034.21 42862.00 51402.81
## 402 403 404 405 406 407 408 409
## 50408.45 49380.08 55747.95 48329.36 47644.11 45979.52 41679.32 40516.14
## 410 411 412 413 414 415 416 417
## 38590.11 32715.08 40546.80 43457.87 38157.98 33271.84 48065.94 51890.68
## 418 419 420 421 422 423 424 425
## 55799.47 48307.89 44650.36 43333.24 46901.01 35380.48 35111.10 29399.80
## 426 427 428 429 430 431 432 433
## 34960.51 43244.73 50100.96 46883.26 43967.22 40908.42 40797.19 37293.35
## 434 435 436 437 438 439 440 441
## 33451.84 30700.29 32268.17 34107.92 32122.93 36963.76 43136.97 39906.24
## 442 443 444 445 446 447 448 449
## 39613.76 42604.54 40501.61 44470.36 39742.00 30820.95 29664.47 40962.29
## 450 451 452 453 454 455 456 457
## 40645.49 46267.28 41926.11 42273.95 43886.03 47584.90 37511.83 42335.90
## 458 459 460 461 462 463 464 465
## 37782.95 45312.64 48814.38 51421.31 48167.86 50496.47 50696.95 52434.91
## 466 467 468 469 470 471 472 473
## 51935.12 54863.58 52205.75 57230.67 50525.64 48148.99 46742.49 43384.38
## 474 475 476 477 478 479 480 481
## 47120.99 54545.53 49001.45 50695.84 45511.76 43899.19 46717.41 36186.64
## 482 483 484 485 486 487 488 489
## 29781.66 31641.27 34323.93 35806.15 36766.99 30440.84 42905.95 49531.53
## 490 491 492 493 494 495 496 497
## 56326.32 51065.28 55873.21 63467.20 67258.80 53586.55 44213.39 42248.63
## 498 499 500 501 502 503 504 505
## 42570.15 43357.20 37872.58 40259.04 45532.99 51185.07 51891.05 52001.04
## 506 507 508 509 510 511 512 513
## 45735.71 47068.72 43347.39 46088.52 45755.95 39504.40 40629.60 39809.04
## 514 515 516 517 518 519 520 521
## 40908.91 43535.21 36412.58 31716.50 55481.92 63600.15 67234.10 60647.62
## 522 523 524 525 526 527 528 529
## 61980.13 75495.09 82435.49 57516.07 52412.81 49124.35 53480.54 52989.44
## 530 531 532 533 534 535 536 537
## 43224.12 48189.95 60784.54 55333.94 58713.89 62680.90 59748.85 54805.48
## 538 539 540 541 542 543 544 545
## 48301.80 46964.70 54860.47 54612.19 47210.99 49423.03 49250.60 49940.82
## 546 547 548 549 550 551 552 553
## 40638.54 32516.82 36836.02 44910.31 44708.33 46405.32 40446.37 49412.36
## 554 555 556 557 558 559 560 561
## 50561.64 40394.11 49885.33 57707.12 57075.37 60654.32 56443.63 68142.80
## 562 563 564 565 566 567 568 569
## 84744.37 74887.06 63510.28 67906.44 66040.62 67221.06 58835.05 42907.69
## 570
## 49879.74
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8385
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.270722 0.5556571 3.285394
## t2* 1733.117976 25.3987857 227.987208
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.274141 6.439885 12.93214
## 2 lag_depvar 1412.481473 1742.236298 2146.16124Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Apr 10 00:37:39 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 10 00:37:47 2023
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## =-=-=-=-= Iteration 4000 Mon Apr 10 00:37:55 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Apr 10 00:38:03 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Apr 10 00:38:11 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 10 00:38:19 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 10 00:38:27 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Apr 10 00:38:35 2023
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## =-=-=-=-= Iteration 16000 Mon Apr 10 00:38:43 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Apr 10 00:38:51 2023
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 0.000000 | 5.410333 | 5.629750 | 6.5222308 |
| Comida | 402.177667 | 310.278417 | 314.087500 | 345.1240256 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 27.629000 | 47.072333 | 38.297667 | 32.0329744 |
| Enceres | 44.300000 | 20.086417 | 17.443792 | 26.1460256 |
| Farmacia | 6.660000 | 1.831667 | 7.913875 | 9.7013846 |
| Gas/Bencina | 23.433333 | 44.325000 | 28.954333 | 24.9308718 |
| Diosi | 16.193333 | 31.180667 | 41.934250 | 38.4732821 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 7.0440769 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 21.2684103 |
| VTR | 7.330000 | 25.156667 | 22.121792 | 20.0584103 |
| Netflix | 5.546667 | 7.151583 | 7.090167 | 7.3274615 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.9695641 |
| Total | 533.270000 | 499.588167 | 522.488250 | 539.5987179 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1925, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-04-09 00:04:58 sería de: 35.440 pesos// Percentil 95% más alto proyectado: 38.819,45
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 35562.07 | 35561.61 |
| Lo.80 | 35562.74 | 35562.43 |
| Point.Forecast | 35564.01 | 35564.00 |
| Hi.80 | 37322.24 | 39884.86 |
| Hi.95 | 38359.03 | 42352.99 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2715 1006.339
## s.e. 0.1419 32.068
##
## sigma^2 = 28637: log likelihood = -326.53
## AIC=659.05 AICc=659.57 BIC=664.79paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3204 33.2930
## s.e. 0.1353 1.0722
##
## sigma^2 = 25818: log likelihood = -323.95
## AIC=653.9 AICc=654.42 BIC=659.64forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 945.0892 | 661.7235 | 706.4674 |
| Lo.80 | 1060.1633 | 781.0071 | 820.3702 |
| Point.Forecast | 1277.5433 | 1006.3390 | 1035.5378 |
| Hi.80 | 1494.9233 | 1231.6709 | 1293.3154 |
| Hi.95 | 1609.9973 | 1350.9545 | 1429.7746 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.27 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.1 xts_0.13.0
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.0 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.1 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
## [28] sjPlot_2.8.14 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.1 httr_1.4.5
## [34] readxl_1.4.2 zoo_1.8-11 stringr_1.5.0
## [37] stringi_1.7.12 DataExplorer_0.8.2 data.table_1.14.8
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.8
## [43] readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 igraph_1.4.2 lazyeval_0.2.2
## [7] splines_4.1.2 crosstalk_1.2.0 digest_0.6.31
## [10] htmltools_0.5.5 fansi_1.0.4 ggfortify_0.4.16
## [13] magrittr_2.0.3 tzdb_0.3.0 modelr_0.1.11
## [16] vroom_1.6.1 timechange_0.2.0 anytime_0.3.9
## [19] tseries_0.10-53 colorspace_2.1-0 xfun_0.38
## [22] crayon_1.5.2 jsonlite_1.8.4 lme4_1.1-32
## [25] glue_1.6.2 r2d3_0.2.6 gtable_0.3.3
## [28] emmeans_1.8.5 sjstats_0.18.2 sjmisc_2.8.9
## [31] car_3.1-2 quantmod_0.4.22 abind_1.4-5
## [34] mvtnorm_1.1-3 DBI_1.1.3 ggeffects_1.2.1
## [37] Rcpp_1.0.10 viridisLite_0.4.1 xtable_1.8-4
## [40] performance_0.10.3 bit_4.0.5 htmlwidgets_1.6.2
## [43] timeSeries_4021.105 gplots_3.1.3 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.5 dbplyr_2.3.2
## [52] janitor_2.2.0 utf8_1.2.3 tidyselect_1.2.0
## [55] labeling_0.4.2 rlang_1.1.0 munsell_0.5.0
## [58] cellranger_1.1.0 tools_4.1.2 cachem_1.0.7
## [61] cli_3.6.1 generics_0.1.3 sjlabelled_1.2.0
## [64] broom_1.0.4 evaluate_0.20 fastmap_1.1.1
## [67] yaml_2.3.7 knitr_1.42 bit64_4.0.5
## [70] caTools_1.18.2 forge_0.2.0 nlme_3.1-153
## [73] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [76] compiler_4.1.2 rstudioapi_0.14 curl_5.0.0
## [79] bslib_0.4.2 highr_0.10 fBasics_4022.94
## [82] Matrix_1.5-4 its.analysis_1.6.0 nloptr_2.0.3
## [85] urca_1.3-3 vctrs_0.6.1 pillar_1.9.0
## [88] lifecycle_1.0.3 networkD3_0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 estimability_1.4.1 bitops_1.0-7
## [94] insight_0.19.1 R6_2.5.1 KernSmooth_2.23-20
## [97] janeaustenr_1.0.0 codetools_0.2-18 gtools_3.9.4
## [100] boot_1.3-28 MASS_7.3-54 assertthat_0.2.1
## [103] rprojroot_2.0.3 withr_2.5.0 fracdiff_1.5-2
## [106] bayestestR_0.13.1 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4022.108 minqa_1.2.5
## [112] snakecase_0.11.0 rmarkdown_2.21 carData_3.0-5
## [115] TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))